{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 全卷积网络（FCN）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上一节介绍了，我们可以基于语义分割对图像中的每个像素进行类别预测。全卷积网络（fully convolutional network，FCN）采用卷积神经网络实现了从图像像素到像素类别的变换。与之前介绍的卷积神经网络有所不同，全卷积网络通过转置卷积（transposed convolution）层将中间层特征图的高和宽变换回输入图像的尺寸，从而令预测结果与输入图像在空间维（高和宽）上一一对应：给定空间维上的位置，通道维的输出即该位置对应像素的类别预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import mxnet as mx\n",
    "from mxnet import gluon, image, init, nd, autograd\n",
    "from mxnet.gluon import data as gdata, loss as gloss, model_zoo, nn, utils as gutils\n",
    "import numpy as np\n",
    "import sys\n",
    "import time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 转置卷积层\n",
    "顾名思义，转置卷积层得名于矩阵的转置操作。事实上，卷积运算还可以通过矩阵乘法来实现。在下面的例子中，我们定义高和宽分别为4的输入X，以及高和宽分别为3的卷积核K。打印二维卷积运算的输出以及卷积核。可以看到，输出的高和宽分别为2。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[[[348. 393.]\n",
       "    [528. 573.]]]]\n",
       " <NDArray 1x1x2x2 @cpu(0)>,\n",
       " \n",
       " [[[[1. 2. 3.]\n",
       "    [4. 5. 6.]\n",
       "    [7. 8. 9.]]]]\n",
       " <NDArray 1x1x3x3 @cpu(0)>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = nd.arange(1, 17).reshape((1, 1, 4, 4))\n",
    "K = nd.arange(1, 10).reshape((1, 1, 3, 3))\n",
    "conv = nn.Conv2D(channels=1, kernel_size=3)\n",
    "conv.initialize(init.Constant(K))\n",
    "conv(X), K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们将卷积核K改写成含有大量零元素的稀疏矩阵W，即权重矩阵。权重矩阵的形状为(4, 16)，其中的非零元素来自卷积核K中的元素。将输入X逐行连结，得到长度为16的向量。然后将W与向量化的X做矩阵乘法，得到长度为4的向量。对其变形后，我们可以得到和上面卷积运算相同的结果。可见，我们在这个例子中使用矩阵乘法实现了卷积运算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[[[348. 393.]\n",
       "    [528. 573.]]]]\n",
       " <NDArray 1x1x2x2 @cpu(0)>,\n",
       " \n",
       " [[1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9. 0. 0. 0. 0. 0.]\n",
       "  [0. 1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9. 0.]\n",
       "  [0. 0. 0. 0. 0. 1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9.]]\n",
       " <NDArray 4x16 @cpu(0)>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W, k = nd.zeros((4, 16)), nd.zeros(11)\n",
    "k[:3], k[4:7], k[8:] = K[0, 0, 0, :], K[0, 0, 1, :], K[0, 0, 2, :]\n",
    "W[0, 0:11], W[1, 1:12], W[2, 4:15], W[3, 5:16] = k, k, k, k\n",
    "nd.dot(W, X.reshape(16)).reshape((1, 1, 2, 2)), W"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来看一个例子。构造一个卷积层conv，并设输入X的形状为(1, 3, 64, 64)。卷积输出Y的通道数增加到10，但高和宽分别缩小了一半。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10, 32, 32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv = nn.Conv2D(10, kernel_size=4, padding=1, strides=2)\n",
    "conv.initialize()\n",
    "\n",
    "X = nd.random.uniform(shape=(1, 3, 64, 64))\n",
    "Y = conv(X)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们通过创建Conv2DTranspose实例来构造转置卷积层conv_trans。这里我们设conv_trans的卷积核形状、填充以及步幅与conv中的相同，并设输出通道数为3。当输入为卷积层conv的输出Y时，转置卷积层输出与卷积层输入的高和宽相同：转置卷积层将特征图的高和宽分别放大了2倍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 64, 64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_trans = nn.Conv2DTranspose(3, kernel_size=4, padding=1, strides=2)\n",
    "conv_trans.initialize()\n",
    "conv_trans(Y).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在有些文献中，转置卷积也被称为分数步长卷积（fractionally-strided convolution）"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构造模型\n",
    "我们在这里给出全卷积网络模型最基本的设计。如图9.11所示，全卷积网络先使用卷积神经网络抽取图像特征，然后通过 1×1 卷积层将通道数变换为类别个数，最后通过转置卷积层将特征图的高和宽变换为输入图像的尺寸。模型输出与输入图像的高和宽相同，并在空间位置上一一对应：最终输出的通道包含了该空间位置像素的类别预测。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们使用一个基于ImageNet数据集预训练的ResNet-18模型来抽取图像特征，并将该网络实例记为pretrained_net。可以看到，该模型成员变量features的最后两层分别是全局最大池化层GlobalAvgPool2D和样本变平层Flatten，而output模块包含了输出用的全连接层。全卷积网络不需要使用这些层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading C:\\Users\\DaChong\\AppData\\Roaming\\mxnet\\models\\resnet18_v2-a81db45f.zip8194a300-251a-428d-b641-0c244f0adea6 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v2-a81db45f.zip...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(HybridSequential(\n",
       "   (0): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)\n",
       "   (1): Activation(relu)\n",
       "   (2): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW)\n",
       "   (3): Flatten\n",
       " ),\n",
       " Dense(512 -> 1000, linear))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_net = model_zoo.vision.resnet18_v2(pretrained=True)\n",
    "pretrained_net.features[-4:], pretrained_net.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们创建全卷积网络实例net。它复制了pretrained_net实例的成员变量features里除去最后两层的所有层以及预训练得到的模型参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = nn.HybridSequential()\n",
    "for layer in pretrained_net.features[:-2]:\n",
    "    net.add(layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给定高和宽分别为320和480的输入，net的前向计算将输入的高和宽减小至原来的 1/32 ，即10和15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 512, 10, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = nd.random.uniform(shape=(1, 3, 320, 480))\n",
    "net(X).shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们通过 1×1 卷积层将输出通道数变换为Pascal VOC2012数据集的类别个数21。最后，我们需要将特征图的高和宽放大32倍，从而变回输入图像的高和宽。回忆一下“填充和步幅”一节中描述的卷积层输出形状的计算方法。由于 (320−64+16×2+32)/32=10 且 (480−64+16×2+32)/32=15 ，我们构造一个步幅为32的转置卷积层，并将卷积核的高和宽设为64、填充设为16。不难发现，如果步幅为 s 、填充为 s/2 （假设 s/2 为整数）、卷积核的高和宽为 2s ，转置卷积核将输入的高和宽分别放大 s 倍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 21\n",
    "net.add(nn.Conv2D(num_classes, kernel_size=1),\n",
    "        nn.Conv2DTranspose(num_classes, kernel_size=64, padding=16,\n",
    "                           strides=32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 初始化转置卷积层\n",
    "我们已经知道，转置卷积层可以放大特征图。在图像处理中，我们有时需要将图像放大，即上采样（upsample）。上采样的方法有很多，常用的有双线性插值。简单来说，为了得到输出图像在坐标 (x,y) 上的像素，先将该坐标映射到输入图像的坐标 (x′,y′) ，例如，根据输入与输出的尺寸之比来映射。映射后的 x′ 和 y′ 通常是实数。然后，在输入图像上找到与坐标 (x′,y′) 最近的4像素。最后，输出图像在坐标 (x,y) 上的像素依据输入图像上这4像素及其与 (x′,y′) 的相对距离来计算。双线性插值的上采样可以通过由以下bilinear_kernel函数构造的卷积核的转置卷积层来实现。限于篇幅，我们只给出bilinear_kernel函数的实现，不再讨论算法的原理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bilinear_kernel(in_channels, out_channels, kernel_size):\n",
    "    factor = (kernel_size + 1) // 2\n",
    "    if kernel_size % 2 == 1:\n",
    "        center = factor - 1\n",
    "    else:\n",
    "        center = factor - 0.5\n",
    "    og = np.ogrid[:kernel_size, :kernel_size]\n",
    "    filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)\n",
    "    weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size),\n",
    "                      dtype='float32')\n",
    "    weight[range(in_channels), range(out_channels), :, :] = filt\n",
    "    return nd.array(weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来实验一下用转置卷积层实现的双线性插值的上采样。构造一个将输入的高和宽放大2倍的转置卷积层，并将其卷积核用bilinear_kernel函数初始化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_trans = nn.Conv2DTranspose(3, kernel_size=4, padding=1, strides=2)\n",
    "conv_trans.initialize(init.Constant(bilinear_kernel(3, 3, 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = image.imread('Datasets/catdog.jpg')\n",
    "X = img.astype('float32').transpose((2, 0, 1)).expand_dims(axis=0) / 255\n",
    "Y = conv_trans(X)\n",
    "out_img = Y[0].transpose((1, 2, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，转置卷积层将图像的高和宽分别放大2倍,除了坐标刻度不同，和原图看上去没什么两样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input image shape: (561, 728, 3)\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
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       "<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n",
       "<svg height=\"170.656221pt\" version=\"1.1\" viewBox=\"0 0 216.84258 170.656221\" width=\"216.84258pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       " <defs>\r\n",
       "  <style type=\"text/css\">\r\n",
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       "  </style>\r\n",
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       "L 0 0 \r\n",
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       "  </g>\r\n",
       "  <g id=\"axes_1\">\r\n",
       "   <g id=\"patch_2\">\r\n",
       "    <path d=\"M 33.2875 146.778096 \r\n",
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       "   <g clip-path=\"url(#peb07955a70)\">\r\n",
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      "text/plain": [
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg')\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "    \n",
    "set_figsize()\n",
    "print('input image shape:', img.shape)\n",
    "plt.imshow(img.asnumpy());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output image shape: (1122, 1456, 3)\n"
     ]
    },
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     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "print('output image shape:', out_img.shape)\n",
    "plt.imshow(out_img.asnumpy());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在全卷积网络中，我们将转置卷积层初始化为双线性插值的上采样。对于 1×1 卷积层，我们采用Xavier随机初始化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "net[-1].initialize(init.Constant(bilinear_kernel(num_classes, num_classes, 64)))\n",
    "net[-2].initialize(init=init.Xavier())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据集\n",
    "我们用上一节介绍的方法读取数据集（上一节中的语义分割数据集）。这里指定随机裁剪的输出图像的形状为 320×480 ：高和宽都可以被32整除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n",
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128]]\n",
    "\n",
    "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n",
    "               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n",
    "               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']\n",
    "\n",
    "def read_voc_images(root=voc_dir, is_train=True):\n",
    "    txt_fname = '%s/ImageSets/Segmentation/%s' % (\n",
    "        root, 'train.txt' if is_train else 'val.txt')\n",
    "    with open(txt_fname, 'r') as f:\n",
    "        images = f.read().split()\n",
    "    features, labels = [None] * len(images), [None] * len(images)\n",
    "    for i, fname in enumerate(images):\n",
    "        features[i] = image.imread('%s/JPEGImages/%s.jpg' % (root, fname))\n",
    "        labels[i] = image.imread(\n",
    "            '%s/SegmentationClass/%s.png' % (root, fname))\n",
    "    return features, labels\n",
    "\n",
    "def voc_label_indices(colormap, colormap2label):\n",
    "    colormap = colormap.astype('int32')\n",
    "    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n",
    "           + colormap[:, :, 2])\n",
    "    return colormap2label[idx]\n",
    "\n",
    "def voc_rand_crop(feature, label, height, width):\n",
    "    feature, rect = image.random_crop(feature, (width, height))\n",
    "    label = image.fixed_crop(label, *rect)\n",
    "    return feature, label\n",
    "\n",
    "class VOCSegDataset(gdata.Dataset):\n",
    "    def __init__(self, is_train, crop_size, voc_dir, colormap2label):\n",
    "        self.rgb_mean = nd.array([0.485, 0.456, 0.406])\n",
    "        self.rgb_std = nd.array([0.229, 0.224, 0.225])\n",
    "        self.crop_size = crop_size\n",
    "        features, labels = read_voc_images(root=voc_dir, is_train=is_train)\n",
    "        self.features = [self.normalize_image(feature)\n",
    "                         for feature in self.filter(features)]\n",
    "        self.labels = self.filter(labels)\n",
    "        self.colormap2label = colormap2label\n",
    "        print('read ' + str(len(self.features)) + ' examples')\n",
    "\n",
    "    def normalize_image(self, img):\n",
    "        return (img.astype('float32') / 255 - self.rgb_mean) / self.rgb_std\n",
    "\n",
    "    def filter(self, imgs):\n",
    "        return [img for img in imgs if (\n",
    "            img.shape[0] >= self.crop_size[0] and\n",
    "            img.shape[1] >= self.crop_size[1])]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],\n",
    "                                       *self.crop_size)\n",
    "        return (feature.transpose((2, 0, 1)),\n",
    "                voc_label_indices(label, self.colormap2label))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n",
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "crop_size, batch_size, colormap2label = (320, 480), 32, nd.zeros(256**3)\n",
    "for i, cm in enumerate(VOC_COLORMAP):\n",
    "    colormap2label[(cm[0] * 256 + cm[1]) * 256 + cm[2]] = i\n",
    "    \n",
    "voc_dir = voc_dir = \"Datasets/VOCdevkit/VOC2012\"\n",
    "\n",
    "num_workers = 0 if sys.platform.startswith('win32') else 4\n",
    "train_iter = gdata.DataLoader(VOCSegDataset(True, crop_size, voc_dir, colormap2label), \n",
    "                              batch_size, shuffle=True, last_batch='discard', num_workers=num_workers)\n",
    "test_iter = gdata.DataLoader(VOCSegDataset(False, crop_size, voc_dir, colormap2label),\n",
    "                             batch_size,last_batch='discard', num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型\n",
    "现在可以开始训练模型了。这里的损失函数和准确率计算与图像分类中的并没有本质上的不同。因为我们使用转置卷积层的通道来预测像素的类别，所以在SoftmaxCrossEntropyLoss里指定了axis=1（通道维）选项。此外，模型基于每个像素的预测类别是否正确来计算准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def try_all_gpus():\n",
    "    ctxes = []\n",
    "    try:\n",
    "        for i in range(16):  # 假设一台机器上GPU的数量不超过16\n",
    "            ctx = mx.gpu(i)\n",
    "            _ = nd.array([0], ctx=ctx)\n",
    "            ctxes.append(ctx)\n",
    "    except mx.base.MXNetError:\n",
    "        pass\n",
    "    if not ctxes:\n",
    "        ctxes = [mx.cpu()]\n",
    "    return ctxes\n",
    "\n",
    "def _get_batch(batch, ctx):\n",
    "    features, labels = batch\n",
    "    if labels.dtype != features.dtype:\n",
    "        labels = labels.astype(features.dtype)\n",
    "    return (gutils.split_and_load(features, ctx),\n",
    "            gutils.split_and_load(labels, ctx), features.shape[0])\n",
    "\n",
    "def evaluate_accuracy(data_iter, net, ctx=[mx.cpu()]):\n",
    "    if isinstance(ctx, mx.Context):\n",
    "        ctx = [ctx]\n",
    "    acc_sum, n = nd.array([0]), 0\n",
    "    for batch in data_iter:\n",
    "        features, labels, _ = _get_batch(batch, ctx)\n",
    "        for X, y in zip(features, labels):\n",
    "            y = y.astype('float32')\n",
    "            acc_sum += (net(X).argmax(axis=1) == y).sum().copyto(mx.cpu())\n",
    "            n += y.size\n",
    "        acc_sum.wait_to_read()\n",
    "    return acc_sum.asscalar() / n\n",
    "\n",
    "def train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs):\n",
    "    print('training on', ctx)\n",
    "    if isinstance(ctx, mx.Context):\n",
    "        ctx = [ctx]\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n, m, start = 0.0, 0.0, 0, 0, time.time()\n",
    "        for i, batch in enumerate(train_iter):\n",
    "            Xs, ys, batch_size = _get_batch(batch, ctx)\n",
    "            with autograd.record():\n",
    "                y_hats = [net(X) for X in Xs]\n",
    "                ls = [loss(y_hat, y) for y_hat, y in zip(y_hats, ys)]\n",
    "            for l in ls:\n",
    "                l.backward()\n",
    "            trainer.step(batch_size)\n",
    "            train_l_sum += sum([l.sum().asscalar() for l in ls])\n",
    "            n += sum([l.size for l in ls])\n",
    "            train_acc_sum += sum([(y_hat.argmax(axis=1) == y).sum().asscalar()\n",
    "                                 for y_hat, y in zip(y_hats, ys)])\n",
    "            m += sum([y.size for y in ys])\n",
    "        test_acc = evaluate_accuracy(test_iter, net, ctx)\n",
    "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, '\n",
    "              'time %.1f sec'\n",
    "              % (epoch + 1, train_l_sum / n, train_acc_sum / m, test_acc,\n",
    "                 time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on [cpu(0)]\n",
      "epoch 1, loss 1.2144, train acc 0.746, test acc 0.800, time 1473.4 sec\n",
      "epoch 2, loss 0.5241, train acc 0.838, test acc 0.834, time 1467.2 sec\n",
      "epoch 3, loss 0.3847, train acc 0.874, test acc 0.846, time 1470.7 sec\n",
      "epoch 4, loss 0.3398, train acc 0.887, test acc 0.854, time 1463.0 sec\n",
      "epoch 5, loss 0.2768, train acc 0.907, test acc 0.852, time 1495.7 sec\n"
     ]
    }
   ],
   "source": [
    "ctx = try_all_gpus()\n",
    "loss = gloss.SoftmaxCrossEntropyLoss(axis=1)\n",
    "net.collect_params().reset_ctx(ctx)\n",
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1, 'wd': 1e-3})\n",
    "train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测像素类别\n",
    "在预测时，我们需要将输入图像在各个通道做标准化，并转成卷积神经网络所需要的四维输入格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(img):\n",
    "    X = test_iter._dataset.normalize_image(img)\n",
    "    X = X.transpose((2, 0, 1)).expand_dims(axis=0)\n",
    "    pred = nd.argmax(net(X.as_in_context(ctx[0])), axis=1)\n",
    "    return pred.reshape((pred.shape[1], pred.shape[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了可视化每个像素的预测类别，我们将预测类别映射回它们在数据集中的标注颜色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label2image(pred):\n",
    "    colormap = nd.array(VOC_COLORMAP, ctx=ctx[0], dtype='uint8')\n",
    "    X = pred.astype('int32')\n",
    "    return colormap[X, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试数据集中的图像大小和形状各异。由于模型使用了步幅为32的转置卷积层，当输入图像的高或宽无法被32整除时，转置卷积层输出的高或宽会与输入图像的尺寸有偏差。为了解决这个问题，我们可以在图像中截取多块高和宽为32的整数倍的矩形区域，并分别对这些区域中的像素做前向计算。这些区域的并集需要完整覆盖输入图像。当一个像素被多个区域所覆盖时，它在不同区域前向计算中转置卷积层输出的平均值可以作为softmax运算的输入，从而预测类别。\n",
    "\n",
    "简单起见，我们只读取几张较大的测试图像，并从图像的左上角开始截取形状为 320×480 的区域：只有该区域用于预测。对于输入图像，我们先打印截取的区域，再打印预测结果，最后打印标注的类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images, test_labels = read_voc_images(is_train=False)\n",
    "n, imgs = 4, []\n",
    "for i in range(n):\n",
    "    crop_rect = (0, 0, 480, 320)\n",
    "    X = image.fixed_crop(test_images[i], *crop_rect)\n",
    "    pred = label2image(predict(X))\n",
    "    imgs += [X, pred, image.fixed_crop(test_labels[i], *crop_rect)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 576x432 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def show_images(imgs, num_rows, num_cols, scale=2):\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    for i in range(num_rows):\n",
    "        for j in range(num_cols):\n",
    "            axes[i][j].imshow(imgs[i * num_cols + j].asnumpy())\n",
    "            axes[i][j].axes.get_xaxis().set_visible(False)\n",
    "            axes[i][j].axes.get_yaxis().set_visible(False)\n",
    "    return axes\n",
    "\n",
    "show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "1、可以通过矩阵乘法来实现卷积运算\n",
    "\n",
    "2、全卷积网络先使用卷积神经网络抽取图像特征，然后通过1x1卷积层将通道数变为类别个数，最后通过转置卷积层将特征图的高和宽变换为输入图像的尺寸，从而输出每个像素类别\n",
    "\n",
    "3、在全卷积网络中，可以将转置卷积层初始化为双线性插值的上采样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 思考练习\n",
    "1、用矩阵乘法来实现卷积运算是否高效？为什么？\n",
    "2、如果将转置卷积层改用Xavier随机初始化，结果有什么变化？\n",
    "3、调节超参数，能进一步提升模型的精度吗？\n",
    "4、预测测试图像中所有像素的类别。\n",
    "5、全卷积网络的论文中还使用了卷积神经网络的某些中间层的输出。试着实现这个想法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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